"""simple docstring"""


from __future__ import annotations

from collections.abc import Callable





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 100  ,       )				->   float:




						'''simple docstring'''



						lowerCamelCase__   =x_start
						lowerCamelCase__   =fnc(__lowerCAmelCase							)
						lowerCamelCase__   =0.0
						for _ in range(__lowerCAmelCase							):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												lowerCamelCase__   =(x_end - x_start) / steps + xa
												lowerCamelCase__   =fnc(__lowerCAmelCase							)
												area += abs(fxa + fxa							) * (xa - xa) / 2
												# Increment step
												lowerCamelCase__   =xa
												lowerCamelCase__   =fxa
						return area


if __name__ == "__main__":





	def      lowerCamelCase_					(   __lowerCAmelCase							)				->   int:




							'''simple docstring'''



							return x**3 + x**2

	print('f(x) = x^3 + x^2')
	print('The area between the curve, x = -5, x = 5 and the x axis is:')
	a				=10
	while i <= 10_0000:
		print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


